Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f88cf77dc50>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f88cf6bd2b0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
/home/areej/.conda/envs/py35/lib/python3.5/site-packages/ipykernel/__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_input = tf.placeholder(tf.float32,[None,image_width, image_height, image_channels], name = 'real_input')
    z_input = tf.placeholder(tf.float32,[None, z_dim], name = 'z_input')
    learning_rate = tf.placeholder(tf.float32,[], name = 'learning_rate')

    return real_input, z_input, learning_rate


tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    alpha = 0.1
    
    with tf.variable_scope('discriminator', reuse=reuse):
        
        c1 = tf.layers.conv2d(images, 56, 5, 2, 'SAME')
        c1 = tf.maximum(alpha*c1, c1)
        
        c2 = tf.layers.conv2d(c1, 112, 5, 2, 'SAME')
        c2 = tf.layers.batch_normalization(c2, training=True)
        c2 = tf.maximum(alpha*c2, c2)

        c3 = tf.layers.conv2d(c2, 224, 5, 2, 'SAME')
        c3 = tf.layers.batch_normalization(c3, training=True)
        c3 = tf.maximum(alpha*c3, c3)
        
        flat   = tf.reshape(c3, (-1, 4*4*224))
        logits = tf.layers.dense(flat, 1)
        
        logits = tf.layers.dropout(logits, rate=0.5)
        
        output = tf.sigmoid(logits)
        
    return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    alpha = 0.1
    
    with tf.variable_scope('generator', reuse=not(is_train)):
        
        full_layer = tf.layers.dense(z, 7*7*112)
        full_layer = tf.reshape(full_layer, (-1, 7, 7, 112))
        full_layer = tf.layers.batch_normalization(full_layer, training=is_train)
        full_layer = tf.maximum(alpha*full_layer, full_layer)
        
        c1 = tf.layers.conv2d_transpose(full_layer, 56, 5, 2, 'SAME')
        c1 = tf.layers.batch_normalization(c1, training=is_train)
        c1 = tf.maximum(alpha*c1, c1)
        
        c2 = tf.layers.conv2d_transpose(c1, out_channel_dim, 5, 2, 'SAME')
        out = tf.tanh(c2)

    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE

"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
import numpy as np
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_ = generator(input_z, out_channel_dim, is_train = True)
    d_real, d_logits_real = discriminator(input_real, reuse = False)
    d_fake, d_logits_fake = discriminator(g_, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_real)*0.9))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    g_updates = [opt for opt in update_ops if opt.name.startswith('generator')]
    with tf.control_dependencies(g_updates):
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1).minimize(g_loss, var_list=g_vars)


    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [16]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model

    image_channels = 1
    if(data_image_mode == 'RGB'):
        image_channels = 3
        
    image_width = data_shape[1]
    image_height = data_shape[2]
    
    
    input_real, input_z, learn_rate = model_inputs(image_width, image_height, image_channels, z_dim)
    learn_rate = learning_rate
    d_loss, g_loss = model_loss(input_real, input_z, image_channels )
    d_train_operation, g_train_operation = model_opt(d_loss, g_loss, learn_rate, beta1)

    itt = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_im in get_batches(batch_size):
                # TODO: Train Model
                itt += 1
                batch_im *= 2.0
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                _ = sess.run(d_train_operation, feed_dict={input_real: batch_im, input_z: batch_z})
                _ = sess.run(g_train_operation, feed_dict={input_z: batch_z})                
                _ = sess.run(g_train_operation, feed_dict={input_z: batch_z})
                if (itt%100 == 0):
                    show_generator_output(sess, 25, input_z, image_channels, data_image_mode)
                if (itt%10 == 0):
                    train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_im})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    print("\nEpoch {}/{} and iteration {}".format(epoch_i+1, epochs, itt),
                          "The discriminator Loss is {:.3f}...".format(train_loss_d),
                              "The generator Loss is {:.3f}".format(train_loss_g))   
            #get the losses and print
            train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_im})
            train_loss_g = g_loss.eval({input_z: batch_z})
            print("\nEpoch {}/{}...".format(epoch_i+1, epochs),
                  "The discriminator Loss is {:.5f}...".format(train_loss_d),
                  "The generator Loss: {:.5f}".format(train_loss_g))

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [17]:
batch_size = 16
z_dim = 100
learning_rate = 0.001
beta1 = 0.45


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2 and iteration 10 The discriminator Loss is 3.761... The generator Loss is 0.378

Epoch 1/2 and iteration 20 The discriminator Loss is 2.846... The generator Loss is 0.149

Epoch 1/2 and iteration 30 The discriminator Loss is 2.663... The generator Loss is 0.136

Epoch 1/2 and iteration 40 The discriminator Loss is 2.206... The generator Loss is 0.255

Epoch 1/2 and iteration 50 The discriminator Loss is 2.185... The generator Loss is 0.265

Epoch 1/2 and iteration 60 The discriminator Loss is 2.103... The generator Loss is 0.268

Epoch 1/2 and iteration 70 The discriminator Loss is 1.876... The generator Loss is 0.364

Epoch 1/2 and iteration 80 The discriminator Loss is 1.852... The generator Loss is 0.336

Epoch 1/2 and iteration 90 The discriminator Loss is 1.753... The generator Loss is 0.580
Epoch 1/2 and iteration 100 The discriminator Loss is 1.544... The generator Loss is 1.160

Epoch 1/2 and iteration 110 The discriminator Loss is 1.716... The generator Loss is 0.657

Epoch 1/2 and iteration 120 The discriminator Loss is 2.180... The generator Loss is 0.204

Epoch 1/2 and iteration 130 The discriminator Loss is 1.563... The generator Loss is 0.508

Epoch 1/2 and iteration 140 The discriminator Loss is 2.189... The generator Loss is 0.184

Epoch 1/2 and iteration 150 The discriminator Loss is 2.133... The generator Loss is 0.202

Epoch 1/2 and iteration 160 The discriminator Loss is 1.994... The generator Loss is 0.653

Epoch 1/2 and iteration 170 The discriminator Loss is 1.922... The generator Loss is 0.303

Epoch 1/2 and iteration 180 The discriminator Loss is 1.613... The generator Loss is 0.677

Epoch 1/2 and iteration 190 The discriminator Loss is 1.538... The generator Loss is 0.644
Epoch 1/2 and iteration 200 The discriminator Loss is 2.340... The generator Loss is 0.195

Epoch 1/2 and iteration 210 The discriminator Loss is 1.521... The generator Loss is 0.761

Epoch 1/2 and iteration 220 The discriminator Loss is 2.028... The generator Loss is 0.254

Epoch 1/2 and iteration 230 The discriminator Loss is 1.723... The generator Loss is 0.392

Epoch 1/2 and iteration 240 The discriminator Loss is 1.730... The generator Loss is 0.533

Epoch 1/2 and iteration 250 The discriminator Loss is 1.811... The generator Loss is 0.519

Epoch 1/2 and iteration 260 The discriminator Loss is 2.253... The generator Loss is 0.202

Epoch 1/2 and iteration 270 The discriminator Loss is 1.382... The generator Loss is 0.837

Epoch 1/2 and iteration 280 The discriminator Loss is 1.697... The generator Loss is 0.339

Epoch 1/2 and iteration 290 The discriminator Loss is 1.675... The generator Loss is 0.493
Epoch 1/2 and iteration 300 The discriminator Loss is 1.867... The generator Loss is 0.264

Epoch 1/2 and iteration 310 The discriminator Loss is 1.452... The generator Loss is 0.832

Epoch 1/2 and iteration 320 The discriminator Loss is 1.590... The generator Loss is 0.593

Epoch 1/2 and iteration 330 The discriminator Loss is 2.011... The generator Loss is 0.292

Epoch 1/2 and iteration 340 The discriminator Loss is 1.456... The generator Loss is 0.978

Epoch 1/2 and iteration 350 The discriminator Loss is 1.949... The generator Loss is 0.256

Epoch 1/2 and iteration 360 The discriminator Loss is 1.550... The generator Loss is 0.456

Epoch 1/2 and iteration 370 The discriminator Loss is 1.393... The generator Loss is 1.192

Epoch 1/2 and iteration 380 The discriminator Loss is 1.810... The generator Loss is 0.476

Epoch 1/2 and iteration 390 The discriminator Loss is 1.451... The generator Loss is 0.515
Epoch 1/2 and iteration 400 The discriminator Loss is 1.624... The generator Loss is 0.417

Epoch 1/2 and iteration 410 The discriminator Loss is 2.065... The generator Loss is 0.237

Epoch 1/2 and iteration 420 The discriminator Loss is 1.972... The generator Loss is 0.236

Epoch 1/2 and iteration 430 The discriminator Loss is 1.983... The generator Loss is 0.228

Epoch 1/2 and iteration 440 The discriminator Loss is 1.453... The generator Loss is 1.125

Epoch 1/2 and iteration 450 The discriminator Loss is 1.786... The generator Loss is 0.352

Epoch 1/2 and iteration 460 The discriminator Loss is 1.557... The generator Loss is 0.410

Epoch 1/2 and iteration 470 The discriminator Loss is 1.909... The generator Loss is 0.273

Epoch 1/2 and iteration 480 The discriminator Loss is 2.229... The generator Loss is 0.196

Epoch 1/2 and iteration 490 The discriminator Loss is 1.292... The generator Loss is 0.961
Epoch 1/2 and iteration 500 The discriminator Loss is 1.917... The generator Loss is 0.285

Epoch 1/2 and iteration 510 The discriminator Loss is 1.538... The generator Loss is 0.439

Epoch 1/2 and iteration 520 The discriminator Loss is 1.647... The generator Loss is 0.385

Epoch 1/2 and iteration 530 The discriminator Loss is 2.063... The generator Loss is 0.245

Epoch 1/2 and iteration 540 The discriminator Loss is 1.472... The generator Loss is 0.866

Epoch 1/2 and iteration 550 The discriminator Loss is 1.488... The generator Loss is 0.478

Epoch 1/2 and iteration 560 The discriminator Loss is 1.663... The generator Loss is 0.383

Epoch 1/2 and iteration 570 The discriminator Loss is 1.486... The generator Loss is 1.139

Epoch 1/2 and iteration 580 The discriminator Loss is 1.836... The generator Loss is 0.304

Epoch 1/2 and iteration 590 The discriminator Loss is 1.473... The generator Loss is 0.541
Epoch 1/2 and iteration 600 The discriminator Loss is 1.523... The generator Loss is 0.499

Epoch 1/2 and iteration 610 The discriminator Loss is 1.441... The generator Loss is 1.026

Epoch 1/2 and iteration 620 The discriminator Loss is 1.347... The generator Loss is 0.711

Epoch 1/2 and iteration 630 The discriminator Loss is 1.332... The generator Loss is 0.829

Epoch 1/2 and iteration 640 The discriminator Loss is 1.306... The generator Loss is 0.600

Epoch 1/2 and iteration 650 The discriminator Loss is 1.816... The generator Loss is 0.286

Epoch 1/2 and iteration 660 The discriminator Loss is 1.436... The generator Loss is 0.609

Epoch 1/2 and iteration 670 The discriminator Loss is 1.463... The generator Loss is 0.565

Epoch 1/2 and iteration 680 The discriminator Loss is 1.769... The generator Loss is 0.544

Epoch 1/2 and iteration 690 The discriminator Loss is 1.726... The generator Loss is 0.385
Epoch 1/2 and iteration 700 The discriminator Loss is 1.261... The generator Loss is 0.771

Epoch 1/2 and iteration 710 The discriminator Loss is 1.610... The generator Loss is 0.400

Epoch 1/2 and iteration 720 The discriminator Loss is 1.490... The generator Loss is 0.885

Epoch 1/2 and iteration 730 The discriminator Loss is 1.859... The generator Loss is 1.436

Epoch 1/2 and iteration 740 The discriminator Loss is 1.705... The generator Loss is 0.353

Epoch 1/2 and iteration 750 The discriminator Loss is 1.727... The generator Loss is 1.506

Epoch 1/2 and iteration 760 The discriminator Loss is 1.617... The generator Loss is 0.526

Epoch 1/2 and iteration 770 The discriminator Loss is 1.726... The generator Loss is 0.348

Epoch 1/2 and iteration 780 The discriminator Loss is 2.318... The generator Loss is 0.171

Epoch 1/2 and iteration 790 The discriminator Loss is 5.030... The generator Loss is 4.720
Epoch 1/2 and iteration 800 The discriminator Loss is 1.764... The generator Loss is 0.364

Epoch 1/2 and iteration 810 The discriminator Loss is 1.442... The generator Loss is 1.161

Epoch 1/2 and iteration 820 The discriminator Loss is 1.589... The generator Loss is 0.423

Epoch 1/2 and iteration 830 The discriminator Loss is 1.277... The generator Loss is 0.635

Epoch 1/2 and iteration 840 The discriminator Loss is 1.914... The generator Loss is 0.279

Epoch 1/2 and iteration 850 The discriminator Loss is 1.525... The generator Loss is 0.470

Epoch 1/2 and iteration 860 The discriminator Loss is 0.904... The generator Loss is 1.260

Epoch 1/2 and iteration 870 The discriminator Loss is 1.467... The generator Loss is 0.532

Epoch 1/2 and iteration 880 The discriminator Loss is 1.253... The generator Loss is 0.715

Epoch 1/2 and iteration 890 The discriminator Loss is 1.666... The generator Loss is 0.363
Epoch 1/2 and iteration 900 The discriminator Loss is 1.378... The generator Loss is 0.498

Epoch 1/2 and iteration 910 The discriminator Loss is 1.358... The generator Loss is 0.911

Epoch 1/2 and iteration 920 The discriminator Loss is 1.826... The generator Loss is 0.333

Epoch 1/2 and iteration 930 The discriminator Loss is 1.167... The generator Loss is 0.821

Epoch 1/2 and iteration 940 The discriminator Loss is 1.141... The generator Loss is 0.737

Epoch 1/2 and iteration 950 The discriminator Loss is 1.129... The generator Loss is 0.774

Epoch 1/2 and iteration 960 The discriminator Loss is 1.762... The generator Loss is 0.326

Epoch 1/2 and iteration 970 The discriminator Loss is 1.790... The generator Loss is 0.332

Epoch 1/2 and iteration 980 The discriminator Loss is 1.400... The generator Loss is 1.032

Epoch 1/2 and iteration 990 The discriminator Loss is 1.662... The generator Loss is 0.381
Epoch 1/2 and iteration 1000 The discriminator Loss is 1.946... The generator Loss is 2.919

Epoch 1/2 and iteration 1010 The discriminator Loss is 1.964... The generator Loss is 0.283

Epoch 1/2 and iteration 1020 The discriminator Loss is 1.856... The generator Loss is 0.296

Epoch 1/2 and iteration 1030 The discriminator Loss is 1.743... The generator Loss is 0.331

Epoch 1/2 and iteration 1040 The discriminator Loss is 1.729... The generator Loss is 0.446

Epoch 1/2 and iteration 1050 The discriminator Loss is 1.702... The generator Loss is 0.411

Epoch 1/2 and iteration 1060 The discriminator Loss is 1.272... The generator Loss is 1.233

Epoch 1/2 and iteration 1070 The discriminator Loss is 1.354... The generator Loss is 0.707

Epoch 1/2 and iteration 1080 The discriminator Loss is 1.306... The generator Loss is 0.600

Epoch 1/2 and iteration 1090 The discriminator Loss is 1.821... The generator Loss is 0.337
Epoch 1/2 and iteration 1100 The discriminator Loss is 1.842... The generator Loss is 0.300

Epoch 1/2 and iteration 1110 The discriminator Loss is 1.268... The generator Loss is 0.757

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Epoch 2/2 and iteration 7420 The discriminator Loss is 2.142... The generator Loss is 0.274

Epoch 2/2 and iteration 7430 The discriminator Loss is 1.120... The generator Loss is 0.742

Epoch 2/2 and iteration 7440 The discriminator Loss is 0.910... The generator Loss is 0.898

Epoch 2/2 and iteration 7450 The discriminator Loss is 1.408... The generator Loss is 0.476

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Epoch 2/2 and iteration 7480 The discriminator Loss is 1.270... The generator Loss is 0.622

Epoch 2/2 and iteration 7490 The discriminator Loss is 1.717... The generator Loss is 0.344
Epoch 2/2 and iteration 7500 The discriminator Loss is 1.088... The generator Loss is 0.893

Epoch 2/2... The discriminator Loss is 1.08829... The generator Loss: 0.89261

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.001
beta1 = 0.45


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1 and iteration 10 The discriminator Loss is 5.967... The generator Loss is 0.014

Epoch 1/1 and iteration 20 The discriminator Loss is 3.046... The generator Loss is 0.097

Epoch 1/1 and iteration 30 The discriminator Loss is 2.071... The generator Loss is 0.279

Epoch 1/1 and iteration 40 The discriminator Loss is 2.197... The generator Loss is 0.229

Epoch 1/1 and iteration 50 The discriminator Loss is 2.149... The generator Loss is 0.873

Epoch 1/1 and iteration 60 The discriminator Loss is 2.037... The generator Loss is 0.465

Epoch 1/1 and iteration 70 The discriminator Loss is 2.167... The generator Loss is 0.260

Epoch 1/1 and iteration 80 The discriminator Loss is 2.118... The generator Loss is 0.461

Epoch 1/1 and iteration 90 The discriminator Loss is 1.730... The generator Loss is 0.464
Epoch 1/1 and iteration 100 The discriminator Loss is 1.626... The generator Loss is 0.599

Epoch 1/1 and iteration 110 The discriminator Loss is 1.506... The generator Loss is 0.668

Epoch 1/1 and iteration 120 The discriminator Loss is 1.449... The generator Loss is 0.620

Epoch 1/1 and iteration 130 The discriminator Loss is 1.661... The generator Loss is 0.648

Epoch 1/1 and iteration 140 The discriminator Loss is 1.660... The generator Loss is 0.470

Epoch 1/1 and iteration 150 The discriminator Loss is 1.343... The generator Loss is 0.922

Epoch 1/1 and iteration 160 The discriminator Loss is 1.559... The generator Loss is 0.511

Epoch 1/1 and iteration 170 The discriminator Loss is 1.657... The generator Loss is 0.460

Epoch 1/1 and iteration 180 The discriminator Loss is 1.512... The generator Loss is 0.625

Epoch 1/1 and iteration 190 The discriminator Loss is 1.671... The generator Loss is 0.482
Epoch 1/1 and iteration 200 The discriminator Loss is 1.477... The generator Loss is 0.541

Epoch 1/1 and iteration 210 The discriminator Loss is 1.587... The generator Loss is 0.880

Epoch 1/1 and iteration 220 The discriminator Loss is 1.585... The generator Loss is 0.834

Epoch 1/1 and iteration 230 The discriminator Loss is 1.475... The generator Loss is 0.738

Epoch 1/1 and iteration 240 The discriminator Loss is 1.507... The generator Loss is 0.706

Epoch 1/1 and iteration 250 The discriminator Loss is 1.474... The generator Loss is 0.660

Epoch 1/1 and iteration 260 The discriminator Loss is 1.487... The generator Loss is 0.629

Epoch 1/1 and iteration 270 The discriminator Loss is 1.808... The generator Loss is 0.646

Epoch 1/1 and iteration 280 The discriminator Loss is 1.558... The generator Loss is 0.759

Epoch 1/1 and iteration 290 The discriminator Loss is 1.514... The generator Loss is 0.529
Epoch 1/1 and iteration 300 The discriminator Loss is 1.581... The generator Loss is 0.606

Epoch 1/1 and iteration 310 The discriminator Loss is 1.722... The generator Loss is 0.588

Epoch 1/1 and iteration 320 The discriminator Loss is 1.489... The generator Loss is 0.571

Epoch 1/1 and iteration 330 The discriminator Loss is 1.384... The generator Loss is 0.877

Epoch 1/1 and iteration 340 The discriminator Loss is 1.787... The generator Loss is 0.420

Epoch 1/1 and iteration 350 The discriminator Loss is 1.432... The generator Loss is 0.661

Epoch 1/1 and iteration 360 The discriminator Loss is 1.546... The generator Loss is 0.583

Epoch 1/1 and iteration 370 The discriminator Loss is 1.656... The generator Loss is 0.619

Epoch 1/1 and iteration 380 The discriminator Loss is 1.516... The generator Loss is 0.592

Epoch 1/1 and iteration 390 The discriminator Loss is 1.471... The generator Loss is 0.568
Epoch 1/1 and iteration 400 The discriminator Loss is 1.423... The generator Loss is 0.620

Epoch 1/1 and iteration 410 The discriminator Loss is 1.527... The generator Loss is 0.732

Epoch 1/1 and iteration 420 The discriminator Loss is 1.439... The generator Loss is 0.709

Epoch 1/1 and iteration 430 The discriminator Loss is 1.484... The generator Loss is 0.646

Epoch 1/1 and iteration 440 The discriminator Loss is 1.543... The generator Loss is 0.757

Epoch 1/1 and iteration 450 The discriminator Loss is 1.432... The generator Loss is 0.757

Epoch 1/1 and iteration 460 The discriminator Loss is 1.532... The generator Loss is 0.614

Epoch 1/1 and iteration 470 The discriminator Loss is 1.598... The generator Loss is 0.505

Epoch 1/1 and iteration 480 The discriminator Loss is 1.351... The generator Loss is 0.793

Epoch 1/1 and iteration 490 The discriminator Loss is 1.573... The generator Loss is 0.754
Epoch 1/1 and iteration 500 The discriminator Loss is 1.442... The generator Loss is 0.706

Epoch 1/1 and iteration 510 The discriminator Loss is 1.535... The generator Loss is 0.658

Epoch 1/1 and iteration 520 The discriminator Loss is 1.464... The generator Loss is 0.498

Epoch 1/1 and iteration 530 The discriminator Loss is 1.564... The generator Loss is 0.458

Epoch 1/1 and iteration 540 The discriminator Loss is 1.338... The generator Loss is 0.734

Epoch 1/1 and iteration 550 The discriminator Loss is 1.687... The generator Loss is 0.689

Epoch 1/1 and iteration 560 The discriminator Loss is 1.388... The generator Loss is 0.643

Epoch 1/1 and iteration 570 The discriminator Loss is 1.505... The generator Loss is 0.706

Epoch 1/1 and iteration 580 The discriminator Loss is 1.414... The generator Loss is 0.716

Epoch 1/1 and iteration 590 The discriminator Loss is 1.461... The generator Loss is 0.763
Epoch 1/1 and iteration 600 The discriminator Loss is 1.469... The generator Loss is 0.839

Epoch 1/1 and iteration 610 The discriminator Loss is 1.497... The generator Loss is 0.571

Epoch 1/1 and iteration 620 The discriminator Loss is 1.425... The generator Loss is 0.726

Epoch 1/1 and iteration 630 The discriminator Loss is 1.615... The generator Loss is 0.606

Epoch 1/1 and iteration 640 The discriminator Loss is 1.510... The generator Loss is 0.734

Epoch 1/1 and iteration 650 The discriminator Loss is 1.439... The generator Loss is 0.594

Epoch 1/1 and iteration 660 The discriminator Loss is 1.512... The generator Loss is 0.555

Epoch 1/1 and iteration 670 The discriminator Loss is 1.403... The generator Loss is 0.622

Epoch 1/1 and iteration 680 The discriminator Loss is 1.409... The generator Loss is 0.756

Epoch 1/1 and iteration 690 The discriminator Loss is 1.527... The generator Loss is 0.613
Epoch 1/1 and iteration 700 The discriminator Loss is 1.526... The generator Loss is 0.571

Epoch 1/1 and iteration 710 The discriminator Loss is 1.550... The generator Loss is 0.601

Epoch 1/1 and iteration 720 The discriminator Loss is 1.280... The generator Loss is 0.959

Epoch 1/1 and iteration 730 The discriminator Loss is 1.481... The generator Loss is 0.595

Epoch 1/1 and iteration 740 The discriminator Loss is 1.326... The generator Loss is 0.857

Epoch 1/1 and iteration 750 The discriminator Loss is 1.462... The generator Loss is 0.811

Epoch 1/1 and iteration 760 The discriminator Loss is 1.642... The generator Loss is 0.612

Epoch 1/1 and iteration 770 The discriminator Loss is 1.509... The generator Loss is 0.652

Epoch 1/1 and iteration 780 The discriminator Loss is 1.375... The generator Loss is 0.654

Epoch 1/1 and iteration 790 The discriminator Loss is 1.556... The generator Loss is 0.744
Epoch 1/1 and iteration 800 The discriminator Loss is 1.432... The generator Loss is 0.606

Epoch 1/1 and iteration 810 The discriminator Loss is 1.571... The generator Loss is 0.474

Epoch 1/1 and iteration 820 The discriminator Loss is 1.652... The generator Loss is 0.364

Epoch 1/1 and iteration 830 The discriminator Loss is 1.567... The generator Loss is 0.565

Epoch 1/1 and iteration 840 The discriminator Loss is 1.530... The generator Loss is 0.737

Epoch 1/1 and iteration 850 The discriminator Loss is 1.471... The generator Loss is 0.806

Epoch 1/1 and iteration 860 The discriminator Loss is 1.599... The generator Loss is 0.688

Epoch 1/1 and iteration 870 The discriminator Loss is 1.496... The generator Loss is 0.686

Epoch 1/1 and iteration 880 The discriminator Loss is 1.446... The generator Loss is 0.718

Epoch 1/1 and iteration 890 The discriminator Loss is 1.359... The generator Loss is 0.875
Epoch 1/1 and iteration 900 The discriminator Loss is 1.550... The generator Loss is 0.758

Epoch 1/1 and iteration 910 The discriminator Loss is 1.504... The generator Loss is 0.522

Epoch 1/1 and iteration 920 The discriminator Loss is 1.483... The generator Loss is 0.762

Epoch 1/1 and iteration 930 The discriminator Loss is 1.618... The generator Loss is 0.762

Epoch 1/1 and iteration 940 The discriminator Loss is 1.548... The generator Loss is 0.733

Epoch 1/1 and iteration 950 The discriminator Loss is 1.489... The generator Loss is 0.717

Epoch 1/1 and iteration 960 The discriminator Loss is 1.444... The generator Loss is 0.728

Epoch 1/1 and iteration 970 The discriminator Loss is 1.437... The generator Loss is 0.613

Epoch 1/1 and iteration 980 The discriminator Loss is 1.448... The generator Loss is 0.647

Epoch 1/1 and iteration 990 The discriminator Loss is 1.392... The generator Loss is 0.854
Epoch 1/1 and iteration 1000 The discriminator Loss is 1.488... The generator Loss is 0.792

Epoch 1/1 and iteration 1010 The discriminator Loss is 1.577... The generator Loss is 0.533

Epoch 1/1 and iteration 1020 The discriminator Loss is 1.403... The generator Loss is 0.989

Epoch 1/1 and iteration 1030 The discriminator Loss is 1.467... The generator Loss is 0.703

Epoch 1/1 and iteration 1040 The discriminator Loss is 1.507... The generator Loss is 0.833

Epoch 1/1 and iteration 1050 The discriminator Loss is 1.503... The generator Loss is 0.640

Epoch 1/1 and iteration 1060 The discriminator Loss is 1.460... The generator Loss is 0.720

Epoch 1/1 and iteration 1070 The discriminator Loss is 1.452... The generator Loss is 0.613

Epoch 1/1 and iteration 1080 The discriminator Loss is 1.562... The generator Loss is 0.567

Epoch 1/1 and iteration 1090 The discriminator Loss is 1.650... The generator Loss is 0.467
Epoch 1/1 and iteration 1100 The discriminator Loss is 1.504... The generator Loss is 0.627

Epoch 1/1 and iteration 1110 The discriminator Loss is 1.418... The generator Loss is 0.773

Epoch 1/1 and iteration 1120 The discriminator Loss is 1.383... The generator Loss is 0.624

Epoch 1/1 and iteration 1130 The discriminator Loss is 1.452... The generator Loss is 0.620

Epoch 1/1 and iteration 1140 The discriminator Loss is 1.598... The generator Loss is 0.542

Epoch 1/1 and iteration 1150 The discriminator Loss is 1.470... The generator Loss is 0.657

Epoch 1/1 and iteration 1160 The discriminator Loss is 1.572... The generator Loss is 0.547

Epoch 1/1 and iteration 1170 The discriminator Loss is 1.471... The generator Loss is 0.656

Epoch 1/1 and iteration 1180 The discriminator Loss is 1.488... The generator Loss is 0.652

Epoch 1/1 and iteration 1190 The discriminator Loss is 1.474... The generator Loss is 0.646
Epoch 1/1 and iteration 1200 The discriminator Loss is 1.518... The generator Loss is 0.571

Epoch 1/1 and iteration 1210 The discriminator Loss is 1.557... The generator Loss is 0.738

Epoch 1/1 and iteration 1220 The discriminator Loss is 1.530... The generator Loss is 0.617

Epoch 1/1 and iteration 1230 The discriminator Loss is 1.553... The generator Loss is 0.556

Epoch 1/1 and iteration 1240 The discriminator Loss is 1.704... The generator Loss is 0.362

Epoch 1/1 and iteration 1250 The discriminator Loss is 1.572... The generator Loss is 0.538

Epoch 1/1 and iteration 1260 The discriminator Loss is 1.452... The generator Loss is 0.649

Epoch 1/1 and iteration 1270 The discriminator Loss is 1.493... The generator Loss is 0.603

Epoch 1/1 and iteration 1280 The discriminator Loss is 1.441... The generator Loss is 0.590

Epoch 1/1 and iteration 1290 The discriminator Loss is 1.376... The generator Loss is 0.611
Epoch 1/1 and iteration 1300 The discriminator Loss is 1.497... The generator Loss is 0.619

Epoch 1/1 and iteration 1310 The discriminator Loss is 1.554... The generator Loss is 0.540

Epoch 1/1 and iteration 1320 The discriminator Loss is 1.605... The generator Loss is 0.626

Epoch 1/1 and iteration 1330 The discriminator Loss is 1.417... The generator Loss is 0.730

Epoch 1/1 and iteration 1340 The discriminator Loss is 1.466... The generator Loss is 0.888

Epoch 1/1 and iteration 1350 The discriminator Loss is 1.464... The generator Loss is 0.742

Epoch 1/1 and iteration 1360 The discriminator Loss is 1.468... The generator Loss is 0.561

Epoch 1/1 and iteration 1370 The discriminator Loss is 1.375... The generator Loss is 0.795

Epoch 1/1 and iteration 1380 The discriminator Loss is 1.504... The generator Loss is 0.633

Epoch 1/1 and iteration 1390 The discriminator Loss is 1.487... The generator Loss is 0.612
Epoch 1/1 and iteration 1400 The discriminator Loss is 1.525... The generator Loss is 0.598

Epoch 1/1 and iteration 1410 The discriminator Loss is 1.450... The generator Loss is 0.783

Epoch 1/1 and iteration 1420 The discriminator Loss is 1.472... The generator Loss is 0.678

Epoch 1/1 and iteration 1430 The discriminator Loss is 1.508... The generator Loss is 0.627

Epoch 1/1 and iteration 1440 The discriminator Loss is 1.355... The generator Loss is 0.824

Epoch 1/1 and iteration 1450 The discriminator Loss is 1.398... The generator Loss is 0.772

Epoch 1/1 and iteration 1460 The discriminator Loss is 1.490... The generator Loss is 0.576

Epoch 1/1 and iteration 1470 The discriminator Loss is 1.597... The generator Loss is 0.615

Epoch 1/1 and iteration 1480 The discriminator Loss is 1.573... The generator Loss is 0.536

Epoch 1/1 and iteration 1490 The discriminator Loss is 1.436... The generator Loss is 0.783
Epoch 1/1 and iteration 1500 The discriminator Loss is 1.545... The generator Loss is 0.964

Epoch 1/1 and iteration 1510 The discriminator Loss is 1.467... The generator Loss is 0.601

Epoch 1/1 and iteration 1520 The discriminator Loss is 1.445... The generator Loss is 0.686

Epoch 1/1 and iteration 1530 The discriminator Loss is 1.460... The generator Loss is 0.700

Epoch 1/1 and iteration 1540 The discriminator Loss is 1.830... The generator Loss is 0.308

Epoch 1/1 and iteration 1550 The discriminator Loss is 1.583... The generator Loss is 0.727

Epoch 1/1 and iteration 1560 The discriminator Loss is 1.414... The generator Loss is 0.795

Epoch 1/1 and iteration 1570 The discriminator Loss is 1.574... The generator Loss is 0.768

Epoch 1/1 and iteration 1580 The discriminator Loss is 1.501... The generator Loss is 0.624

Epoch 1/1 and iteration 1590 The discriminator Loss is 1.595... The generator Loss is 0.578
Epoch 1/1 and iteration 1600 The discriminator Loss is 1.419... The generator Loss is 0.612

Epoch 1/1 and iteration 1610 The discriminator Loss is 1.457... The generator Loss is 0.582

Epoch 1/1 and iteration 1620 The discriminator Loss is 1.485... The generator Loss is 0.611

Epoch 1/1 and iteration 1630 The discriminator Loss is 1.401... The generator Loss is 0.695

Epoch 1/1 and iteration 1640 The discriminator Loss is 1.449... The generator Loss is 0.622

Epoch 1/1 and iteration 1650 The discriminator Loss is 1.446... The generator Loss is 0.630

Epoch 1/1 and iteration 1660 The discriminator Loss is 1.395... The generator Loss is 0.739

Epoch 1/1 and iteration 1670 The discriminator Loss is 1.504... The generator Loss is 0.726

Epoch 1/1 and iteration 1680 The discriminator Loss is 1.514... The generator Loss is 0.689

Epoch 1/1 and iteration 1690 The discriminator Loss is 1.517... The generator Loss is 0.734
Epoch 1/1 and iteration 1700 The discriminator Loss is 1.551... The generator Loss is 0.535

Epoch 1/1 and iteration 1710 The discriminator Loss is 1.447... The generator Loss is 0.555

Epoch 1/1 and iteration 1720 The discriminator Loss is 1.579... The generator Loss is 0.432

Epoch 1/1 and iteration 1730 The discriminator Loss is 1.575... The generator Loss is 0.643

Epoch 1/1 and iteration 1740 The discriminator Loss is 1.394... The generator Loss is 0.670

Epoch 1/1 and iteration 1750 The discriminator Loss is 1.587... The generator Loss is 0.650

Epoch 1/1 and iteration 1760 The discriminator Loss is 1.572... The generator Loss is 0.714

Epoch 1/1 and iteration 1770 The discriminator Loss is 1.515... The generator Loss is 0.572

Epoch 1/1 and iteration 1780 The discriminator Loss is 1.510... The generator Loss is 0.549

Epoch 1/1 and iteration 1790 The discriminator Loss is 1.490... The generator Loss is 0.603
Epoch 1/1 and iteration 1800 The discriminator Loss is 1.591... The generator Loss is 0.601

Epoch 1/1 and iteration 1810 The discriminator Loss is 1.532... The generator Loss is 0.612

Epoch 1/1 and iteration 1820 The discriminator Loss is 1.413... The generator Loss is 0.774

Epoch 1/1 and iteration 1830 The discriminator Loss is 1.562... The generator Loss is 0.548

Epoch 1/1 and iteration 1840 The discriminator Loss is 1.581... The generator Loss is 0.506

Epoch 1/1 and iteration 1850 The discriminator Loss is 1.425... The generator Loss is 0.620

Epoch 1/1 and iteration 1860 The discriminator Loss is 1.526... The generator Loss is 0.568

Epoch 1/1 and iteration 1870 The discriminator Loss is 1.537... The generator Loss is 0.607

Epoch 1/1 and iteration 1880 The discriminator Loss is 1.462... The generator Loss is 0.779

Epoch 1/1 and iteration 1890 The discriminator Loss is 1.564... The generator Loss is 0.471
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Epoch 1/1 and iteration 1910 The discriminator Loss is 1.512... The generator Loss is 0.492

Epoch 1/1 and iteration 1920 The discriminator Loss is 1.425... The generator Loss is 0.702

Epoch 1/1 and iteration 1930 The discriminator Loss is 1.488... The generator Loss is 0.640

Epoch 1/1 and iteration 1940 The discriminator Loss is 1.675... The generator Loss is 0.427

Epoch 1/1 and iteration 1950 The discriminator Loss is 1.574... The generator Loss is 0.667

Epoch 1/1 and iteration 1960 The discriminator Loss is 1.506... The generator Loss is 0.599

Epoch 1/1 and iteration 1970 The discriminator Loss is 1.518... The generator Loss is 0.522

Epoch 1/1 and iteration 1980 The discriminator Loss is 1.419... The generator Loss is 0.577

Epoch 1/1 and iteration 1990 The discriminator Loss is 1.387... The generator Loss is 0.749
Epoch 1/1 and iteration 2000 The discriminator Loss is 1.535... The generator Loss is 0.539

Epoch 1/1 and iteration 2010 The discriminator Loss is 1.535... The generator Loss is 0.569

Epoch 1/1 and iteration 2020 The discriminator Loss is 1.423... The generator Loss is 0.831

Epoch 1/1 and iteration 2030 The discriminator Loss is 1.637... The generator Loss is 0.401

Epoch 1/1 and iteration 2040 The discriminator Loss is 1.602... The generator Loss is 0.652

Epoch 1/1 and iteration 2050 The discriminator Loss is 1.477... The generator Loss is 0.670

Epoch 1/1 and iteration 2060 The discriminator Loss is 1.620... The generator Loss is 0.657

Epoch 1/1 and iteration 2070 The discriminator Loss is 1.489... The generator Loss is 0.633

Epoch 1/1 and iteration 2080 The discriminator Loss is 1.689... The generator Loss is 0.450

Epoch 1/1 and iteration 2090 The discriminator Loss is 1.550... The generator Loss is 0.737
Epoch 1/1 and iteration 2100 The discriminator Loss is 1.572... The generator Loss is 0.544

Epoch 1/1 and iteration 2110 The discriminator Loss is 1.621... The generator Loss is 0.401

Epoch 1/1 and iteration 2120 The discriminator Loss is 1.531... The generator Loss is 0.552

Epoch 1/1 and iteration 2130 The discriminator Loss is 1.584... The generator Loss is 0.473

Epoch 1/1 and iteration 2140 The discriminator Loss is 1.504... The generator Loss is 0.607

Epoch 1/1 and iteration 2150 The discriminator Loss is 1.479... The generator Loss is 0.722

Epoch 1/1 and iteration 2160 The discriminator Loss is 1.496... The generator Loss is 0.616

Epoch 1/1 and iteration 2170 The discriminator Loss is 1.618... The generator Loss is 0.472

Epoch 1/1 and iteration 2180 The discriminator Loss is 1.574... The generator Loss is 0.612

Epoch 1/1 and iteration 2190 The discriminator Loss is 1.449... The generator Loss is 0.714
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Epoch 1/1 and iteration 2210 The discriminator Loss is 1.442... The generator Loss is 0.909

Epoch 1/1 and iteration 2220 The discriminator Loss is 1.548... The generator Loss is 0.656

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Epoch 1/1 and iteration 2240 The discriminator Loss is 1.428... The generator Loss is 0.633

Epoch 1/1 and iteration 2250 The discriminator Loss is 1.575... The generator Loss is 0.501

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Epoch 1/1 and iteration 2270 The discriminator Loss is 1.542... The generator Loss is 0.502

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Epoch 1/1 and iteration 2290 The discriminator Loss is 1.611... The generator Loss is 0.425
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Epoch 1/1 and iteration 2310 The discriminator Loss is 1.477... The generator Loss is 0.618

Epoch 1/1 and iteration 2320 The discriminator Loss is 1.505... The generator Loss is 0.537

Epoch 1/1 and iteration 2330 The discriminator Loss is 1.676... The generator Loss is 0.471

Epoch 1/1 and iteration 2340 The discriminator Loss is 1.542... The generator Loss is 0.557

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Epoch 1/1 and iteration 2360 The discriminator Loss is 1.549... The generator Loss is 0.547

Epoch 1/1 and iteration 2370 The discriminator Loss is 1.591... The generator Loss is 0.545

Epoch 1/1 and iteration 2380 The discriminator Loss is 1.554... The generator Loss is 0.616

Epoch 1/1 and iteration 2390 The discriminator Loss is 1.656... The generator Loss is 0.372
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Epoch 1/1 and iteration 2420 The discriminator Loss is 1.489... The generator Loss is 0.562

Epoch 1/1 and iteration 2430 The discriminator Loss is 1.570... The generator Loss is 0.486

Epoch 1/1 and iteration 2440 The discriminator Loss is 1.478... The generator Loss is 0.540

Epoch 1/1 and iteration 2450 The discriminator Loss is 1.815... The generator Loss is 0.347

Epoch 1/1 and iteration 2460 The discriminator Loss is 1.555... The generator Loss is 0.535

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Epoch 1/1 and iteration 2490 The discriminator Loss is 1.711... The generator Loss is 0.397
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Epoch 1/1 and iteration 2530 The discriminator Loss is 1.512... The generator Loss is 0.582

Epoch 1/1 and iteration 2540 The discriminator Loss is 1.484... The generator Loss is 0.594

Epoch 1/1 and iteration 2550 The discriminator Loss is 1.578... The generator Loss is 0.436

Epoch 1/1 and iteration 2560 The discriminator Loss is 1.620... The generator Loss is 0.442

Epoch 1/1 and iteration 2570 The discriminator Loss is 1.464... The generator Loss is 0.606

Epoch 1/1 and iteration 2580 The discriminator Loss is 1.680... The generator Loss is 0.526

Epoch 1/1 and iteration 2590 The discriminator Loss is 1.651... The generator Loss is 0.429
Epoch 1/1 and iteration 2600 The discriminator Loss is 1.553... The generator Loss is 0.598

Epoch 1/1 and iteration 2610 The discriminator Loss is 1.523... The generator Loss is 0.605

Epoch 1/1 and iteration 2620 The discriminator Loss is 1.489... The generator Loss is 0.606

Epoch 1/1 and iteration 2630 The discriminator Loss is 1.720... The generator Loss is 0.385

Epoch 1/1 and iteration 2640 The discriminator Loss is 1.674... The generator Loss is 0.743

Epoch 1/1 and iteration 2650 The discriminator Loss is 1.709... The generator Loss is 0.476

Epoch 1/1 and iteration 2660 The discriminator Loss is 1.496... The generator Loss is 0.759

Epoch 1/1 and iteration 2670 The discriminator Loss is 1.555... The generator Loss is 0.574

Epoch 1/1 and iteration 2680 The discriminator Loss is 1.514... The generator Loss is 0.696

Epoch 1/1 and iteration 2690 The discriminator Loss is 1.578... The generator Loss is 0.577
Epoch 1/1 and iteration 2700 The discriminator Loss is 1.629... The generator Loss is 0.534

Epoch 1/1 and iteration 2710 The discriminator Loss is 1.555... The generator Loss is 0.424

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

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